\section{Future Work}

Our experiments showed the initial viability of using our approach. It
definitely highlighted some benefits of using this approach in training a naive
bayes classifier which aids in web page design for classes of web pages. It also
opens up new avenues of research.

In our implementation we used Google's PageRank algorithm as the first step in
sorting a list of web pages based on popularity. However using it has its
limitations. First Google's AJAX Search API doesn't allow direct access to the ranking
algorithm. This means that setting the rank source vector for a given search
term depends
on Google's interpretation of the search term. We couldn't tweak the rank source vector precisely
tailored to searches for different classes of web pages. Future
work would use an implementation of PageRank that allows access to the rank
source vector. This would allow experimenting with the approach in specific
classes of web pages, to determine if the approach is viable in specific niches
of web pages.

A second limitation of using Google's AJAX Search API was that it restricted the
size of returned search results. The returned search results count was around 
10-20.
Such a small training data size is clearly insufficient for training the naive
bayes model. A naive bayes model requires training data set of considerably more
size to give more approximate results. 

Secondly, we used a naive bayes model for classification of web pages based
on attributes of web pages. However the naive bayes model makes the assumption
that the probabilities of occurrence of individual attributes of web pages are
independent of each other. This means that any relation between individual
attributes of a web pages are ignored. Future work could focus on using other 
machine learning and classification techniques for better extraction of the 
data.
We expect research in use of other classification models like a non-naive bayes
netowrk, neural networks and markov decisions processes would yield more useful
results. Research in other techniques that can benifit from smaller training 
data set size would also be useful. This is to overcome the limit of the search
results returned by using Google AJAX Search API. 
